Triple-stream Siamese Segmentation Network for Printed Label Defect Detection

Detecting defects in printed labels is essential for quality control. Although a few of vision-based models have been proposed for this challenging task, they fail to deal with the large deformation in printed labels, and can not be generalized well to unseen defects. To this end, we propose a novel Triple-stream Siamese Segmentation Network (TSS-Net) to overcome these issues. TSS-Net utilizes two separate Siamese groups integrated with a registration module to learn differential features based on Siamese similarity comparison, and employs a feature complementary learning strategy, so that the model is able to simultaneously handle large deformations and generalize to unseen defects. Specifically, a pre-trained Registration Module (RM) is integrated into the triple-stream Siamese segmentation network to achieve end-to-end detection, which enhances the robustness to large deformations. Moreover, a group of Differential Feature Enhancement (DFE) modules are constructed to learn differential features at different scales, which forces the network to focus on changed features while ignores invariant features. Further, a feature complementary learning strategy is designed to fuse differential features at different scales, which can suppress artifacts during network reconstruction. Finally, a method is proposed to simulate printed labels with varying degrees of deformation and artificial defects. Extensive experimental results show that our proposed TSS-Net yields the best performance compared with the state-of-the-art method. Specifically, our proposed method achieves improvements of 4.20% on the F 1 score and 7.51% on the Intersection over Union (IoU).